{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Using Word2Vec for prediction\n",
    "------------------------------------\n",
    "\n",
    "In this example, we will load our prior CBOW trained embeddings to perform logistic regression model for movie review predictions.\n",
    "\n",
    "From this data set we will compute/fit the CBOW model of the Word2Vec Algorithm.\n",
    "\n",
    "As a pre-requisite, be sure to have run the prior recipe in section 5 of this chapter, '05_Working_With_CBOW.py'.  This will train and save the word2vec-CBOW embeddings.\n",
    "\n",
    "We start by loading the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "import os\n",
    "import pickle\n",
    "import string\n",
    "import requests\n",
    "import collections\n",
    "import io\n",
    "import tarfile\n",
    "import urllib.request\n",
    "import text_helpers\n",
    "from nltk.corpus import stopwords\n",
    "from tensorflow.python.framework import ops\n",
    "ops.reset_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next we start a computational graph session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Declare model parameters.  An important note here is that the `embedding_size` must be the same size as we set during training in the CBOW example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "embedding_size = 200  # If you change this, you must change it in the CBOW tutorial and retrain.\n",
    "vocabulary_size = 2000\n",
    "batch_size = 100\n",
    "max_words = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next, we declare our stop words, load the data, and normalize the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Data\n",
      "Normalizing Text Data\n"
     ]
    }
   ],
   "source": [
    "# Declare stop words\n",
    "stops = stopwords.words('english')\n",
    "\n",
    "# Load Data\n",
    "print('Loading Data')\n",
    "texts, target = text_helpers.load_movie_data()\n",
    "\n",
    "# Normalize text\n",
    "print('Normalizing Text Data')\n",
    "texts = text_helpers.normalize_text(texts, stops)\n",
    "\n",
    "# Texts must contain at least 3 words\n",
    "target = [target[ix] for ix, x in enumerate(texts) if len(x.split()) > 2]\n",
    "texts = [x for x in texts if len(x.split()) > 2]\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Split the data set into train/test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "train_indices = np.random.choice(len(target), round(0.8*len(target)), replace=False)\n",
    "test_indices = np.array(list(set(range(len(target))) - set(train_indices)))\n",
    "texts_train = [x for ix, x in enumerate(texts) if ix in train_indices]\n",
    "texts_test = [x for ix, x in enumerate(texts) if ix in test_indices]\n",
    "target_train = np.array([x for ix, x in enumerate(target) if ix in train_indices])\n",
    "target_test = np.array([x for ix, x in enumerate(target) if ix in test_indices])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next we load the dictionary file, and convert the review texts into indices and pad/crop them to a fixed length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Load dictionary and embedding matrix\n",
    "dict_file = os.path.join( '..', '05_Working_With_CBOW_Embeddings', 'temp', 'movie_vocab.pkl')\n",
    "word_dictionary = pickle.load(open(dict_file, 'rb'))\n",
    "\n",
    "# Convert texts to lists of indices\n",
    "text_data_train = np.array(text_helpers.text_to_numbers(texts_train, word_dictionary))\n",
    "text_data_test = np.array(text_helpers.text_to_numbers(texts_test, word_dictionary))\n",
    "\n",
    "# Pad/crop movie reviews to specific length\n",
    "text_data_train = np.array([x[0:max_words] for x in [y+[0]*max_words for y in text_data_train]])\n",
    "text_data_test = np.array([x[0:max_words] for x in [y+[0]*max_words for y in text_data_test]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we create the model.  Note that won't matter what we initialize the embedding variable to, because we will overwrite it from the saved CBOW embeddings from the prior recipe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Model\n"
     ]
    }
   ],
   "source": [
    "print('Creating Model')\n",
    "# Define Embeddings:\n",
    "embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "\n",
    "# Define model:\n",
    "# Create variables for logistic regression\n",
    "A = tf.Variable(tf.random_normal(shape=[embedding_size,1]))\n",
    "b = tf.Variable(tf.random_normal(shape=[1,1]))\n",
    "\n",
    "# Initialize placeholders\n",
    "x_data = tf.placeholder(shape=[None, max_words], dtype=tf.int32)\n",
    "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n",
    "\n",
    "# Lookup embeddings vectors\n",
    "embed = tf.nn.embedding_lookup(embeddings, x_data)\n",
    "# Take average of all word embeddings in documents\n",
    "embed_avg = tf.reduce_mean(embed, 1)\n",
    "\n",
    "# Declare logistic model (sigmoid in loss function)\n",
    "model_output = tf.add(tf.matmul(embed_avg, A), b)\n",
    "\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we can declare our loss function, predictions function, optimization, and intialize the variables.\n",
    "\n",
    "After that we will load the model embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Declare loss function (Cross Entropy loss)\n",
    "loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=model_output, labels=y_target))\n",
    "\n",
    "# Actual Prediction\n",
    "prediction = tf.round(tf.sigmoid(model_output))\n",
    "predictions_correct = tf.cast(tf.equal(prediction, y_target), tf.float32)\n",
    "accuracy = tf.reduce_mean(predictions_correct)\n",
    "\n",
    "# Declare optimizer\n",
    "my_opt = tf.train.AdagradOptimizer(0.005)\n",
    "train_step = my_opt.minimize(loss)\n",
    "\n",
    "# Intitialize Variables\n",
    "init = tf.global_variables_initializer()\n",
    "sess.run(init)\n",
    "\n",
    "# Load model embeddings\n",
    "model_checkpoint_path = os.path.join( '..', '05_Working_With_CBOW_Embeddings',\n",
    "                                     'temp','cbow_movie_embeddings.ckpt')\n",
    "saver = tf.train.Saver({\"embeddings\": embeddings})\n",
    "saver.restore(sess, model_checkpoint_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now we can start training our logistic regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Model Training\n",
      "Generation # 500. Train Loss (Test Loss): 0.72 (0.70). Train Acc (Test Acc): 0.43 (0.51)\n",
      "Generation # 1000. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.56 (0.51)\n",
      "Generation # 1500. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.56 (0.51)\n",
      "Generation # 2000. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.44 (0.51)\n",
      "Generation # 2500. Train Loss (Test Loss): 0.71 (0.70). Train Acc (Test Acc): 0.48 (0.51)\n",
      "Generation # 3000. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.57 (0.50)\n",
      "Generation # 3500. Train Loss (Test Loss): 0.71 (0.70). Train Acc (Test Acc): 0.40 (0.52)\n",
      "Generation # 4000. Train Loss (Test Loss): 0.71 (0.70). Train Acc (Test Acc): 0.46 (0.51)\n",
      "Generation # 4500. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.55 (0.51)\n",
      "Generation # 5000. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.51 (0.52)\n",
      "Generation # 5500. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.51 (0.51)\n",
      "Generation # 6000. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.54 (0.52)\n",
      "Generation # 6500. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.54 (0.52)\n",
      "Generation # 7000. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.55 (0.52)\n",
      "Generation # 7500. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.58 (0.52)\n",
      "Generation # 8000. Train Loss (Test Loss): 0.68 (0.70). Train Acc (Test Acc): 0.59 (0.52)\n",
      "Generation # 8500. Train Loss (Test Loss): 0.70 (0.70). Train Acc (Test Acc): 0.57 (0.52)\n",
      "Generation # 9000. Train Loss (Test Loss): 0.71 (0.70). Train Acc (Test Acc): 0.41 (0.52)\n",
      "Generation # 9500. Train Loss (Test Loss): 0.69 (0.70). Train Acc (Test Acc): 0.61 (0.52)\n",
      "Generation # 10000. Train Loss (Test Loss): 0.70 (0.69). Train Acc (Test Acc): 0.49 (0.52)\n"
     ]
    }
   ],
   "source": [
    "# Start Logistic Regression\n",
    "print('Starting Model Training')\n",
    "train_loss = []\n",
    "test_loss = []\n",
    "train_acc = []\n",
    "test_acc = []\n",
    "i_data = []\n",
    "for i in range(10000):\n",
    "    rand_index = np.random.choice(text_data_train.shape[0], size=batch_size)\n",
    "    rand_x = text_data_train[rand_index]\n",
    "    rand_y = np.transpose([target_train[rand_index]])\n",
    "    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "    \n",
    "    # Only record loss and accuracy every 100 generations\n",
    "    if (i+1)%100==0:\n",
    "        i_data.append(i+1)\n",
    "        train_loss_temp = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "        train_loss.append(train_loss_temp)\n",
    "        \n",
    "        test_loss_temp = sess.run(loss, feed_dict={x_data: text_data_test, y_target: np.transpose([target_test])})\n",
    "        test_loss.append(test_loss_temp)\n",
    "        \n",
    "        train_acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x, y_target: rand_y})\n",
    "        train_acc.append(train_acc_temp)\n",
    "    \n",
    "        test_acc_temp = sess.run(accuracy, feed_dict={x_data: text_data_test, y_target: np.transpose([target_test])})\n",
    "        test_acc.append(test_acc_temp)\n",
    "    if (i+1)%500==0:\n",
    "        acc_and_loss = [i+1, train_loss_temp, test_loss_temp, train_acc_temp, test_acc_temp]\n",
    "        acc_and_loss = [np.round(x,2) for x in acc_and_loss]\n",
    "        print('Generation # {}. Train Loss (Test Loss): {:.2f} ({:.2f}). Train Acc (Test Acc): {:.2f} ({:.2f})'.format(*acc_and_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here is the matplotlib code to plot the train/test loss and accuracy over the iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OBfqmsPTv35/MzMywf3g7d+7kwgsvDGsfgWCnG7tTXl5OdXV1n4gh+MNKWPrK\nvCyBuMLuV0rVisg04AzgJeAPAe5/MvCtUmqv09p5A9/WzqXA6/obpdRSIPDx7A0cPHjQzWKJVIxF\n96uDuRUUDn3dFQaRibM0NDRY9vhbWloi8tBua2ujtbWVpKQkO8bipLy8HKVUwCNY9GZsi8U/Duff\n84DnlVIfAt7dTXPygX2G9/udy7wQkaHAcGBJgPv2SbQsFr2XarXPcOgrwmJlsUBk4iyNjY2W1+XF\nF1/k/vvvD2v/0HEfiIjrPgi3p96TK++bm5tpampi4MCBtjsMTViSk5O9lvcVYQlkSJdiEfkTmrXy\nmIgkEnhhpdnYHFa/vlnAWyqEX+eDDz7o+r+wsJDCwkIvi8UqxpKWlgaEJiyR8q3r9CVhiabFoguL\n2fAwlZWVVFRUhLV/cL8P4uLiiImJobW11VIwA8EYvO9pMZby8nJycnLIyMigurqagoKCrm5Sl9LY\n2NjtXGHLli1j2bJlnXKsQITlYuAc4AmlVJWIDCbwOpb9wFDD+wLggMW6s4DrA9yvG0ZhAS1462mx\nmIlAKFlheowFomOx9IXgvVUdC2jCEu7N39jYSHt7O21tbcTHx3t9FsxsoVbogXsdveMSjrD05BiL\nLiz9+/e3LRa6pytM73TrzJ07N2rHCiQrrAHYCZwtIr8CBiilPglw/2uAI0VkmIgkoImHV4aXiIwB\nMpRSX5rsQzC3fCypqakhLi7O1fsHa1eYr6ywxsZG2tra3JZFM8bS14P30DEYZTjo19ns4dzY2Oia\nVz4cjBYLRKaT0ZNjLOXl5eTm5pKRkWELC91TWDqTQLLCbgL+Bgxwvv4qIv8XyM6VUg7gV8AnwGbg\nDaXUVhGZKyLnG1adhRbY9zz258DfgRkiUiQiZwZyXM9UYwit8v6GG27gb3/7m9syO8YSPr6C90cc\ncQRFRUVhZdv5E5ZIWCzREpaeGmPRLZb09HS7SBJrYcnKyqKpqanXJzgE4gq7CjhJKVUPICKPAV+g\npQb7RSn1ETDGY9kcj/emNplS6tRAjuHJwYMHveZCCCV4v3XrVkaNGuW2LNoxlqysLKB3C4sviyUx\nMZFBgwaxb98+t9n3gqEzhMXTFRaJe0F3hSUlJfW4GEtZWRk5OTm0t7fbFgvWwiIirjjL6NGju6Bl\nnUMgQXihIzMM5//desIMM4sllHTjXbt2UV5e7rbMM8ZipxsHj6/gPcCgQYPCGj6/J1ssPdkVpgfv\n+7qwKKUcTioEAAAgAElEQVTc4ree9IVRjgOxWF4GVovIv5zvLwT+HL0mhY+VxeIpAsaLr/eg9d50\nbW0thw4d8hKW+vp60tPTXfu0XWHB4yt4D5Cbm9vthcXYwYDw74W2tjZaWlpcKap68oFeiNvdKS8v\nZ8yYMTQ0NHD48GH/G/RiWltbiYuLIzY21vTzgQMHUlZW1smt6lwCCd4vBH4OHAYqgZ8rpZ6MdsPC\nwcxiiY+Px+Fw4HB0GF+evn6j1bJr1y4Arxsg2q6wvpAV5ssVBjBgwICICIuZNRnJ4L1nVlg494Ie\nXxERt9qYnoIevE9PT+/zFouVG0wnJyfHq8Pa2wioO6SUWgus1d+LSJFSaqiPTbqUgwcPMnnyZLdl\nIuISAv2BYMwKgw5hycrKYteuXQwYMMDUYomWK6wvZYX5coXl5uaG1aPzZ7E0NDTgcDgse5SBEGlX\nmB5f0dFrWYzLujN6jAWwhcUWlpBnkOxxMRbw/vH7slh27tzJSSed5DfGYrvCgsefKyxSFouVsABh\nB8et6lhCRY+vGPfX0ywWY4FkX8afsITbceoJhCos3XqUObMYC3j/+P25wiZPnmxqsegPFLvyPjQC\ncYVF02IBwo6zRNpi0V1hkdpfZ2NMN7YtFttisXSFicitVh8B/S0+6xZYWSyeQuDPYjnvvPNoampy\nWy+arrC+Iiy+6lggMsF7EbEUlpiYGGpraxk8eHDIx6irq3NLh460K6wnCYtSivLycrKzs6mtrbUt\nFltYfFosqRav/sDT0W9aaJgN56Lj+WP1TAn0FJYjjzyS7Oxst7Glol0gaQzeR3JI/kDYvn07b7/9\ntv8Vw6Qzgvfp6emWwpKTkxMRiyWSdSxmrrCeUstSV1dHfHw8ycnJQacbNzQ0MHLkyF5VMGi7wnxY\nLFZFi92dqqoqkpOTTS+smSvMLHjf1tbGvn37GD58uKt3kZenTUFjjLEkJSVRWloasbZ3dfD+nXfe\nYfHixfzoRz+K6nE6I3ivVzibfVZQUNAtXWGewfueYrGUlZWRm5sLELQrbPXq1ezatYuvvvqK0047\nLVpN7FRsiyX0GEu3xcpagcCD9/v27WPgwIEkJiaSm5vrdhP05vlYtmzZwp49e6J+nEDqWMrKykIe\nhl4XFiuLZcCAAWELi1nwvq/GWPT4CmjtdjgcNDc3B7TtihUriI+PZ+XKldFsYqdiNbKxji4svXlC\ntF4nLFaBewg8xrJr1y6X/9yzd+HPFaaUCtnH3B2EZe/evW61PtHAnyssMTGRlJSUkM+jlbA4HA5a\nW1vJzs7udhZLT46xGIVFRILKDFu+fDmzZ8/uVcLiz2JJSkpyFWH3VgIZhDL0ZP8uwCpwD+4WhlLK\nUlh27tzJyJEjAd/CYuZXX7NmDeeffz7B0tbWRltbm+uBGx8fT2tra0R6NW1tbX4fUu3t7WzdupXk\n5GRKSkrCPqYv/LnCILwAfmNjI5mZmabTJCQlJZGamhr2jzrarrCeKiwQuDusra2NL7/8kttvv50v\nvvgi6h2azsJqki8jvd0dFojF8p2IPC4iR0W9NRHAl8Vi/LG2trYSExPjViTnT1iM09F67k+ntLQ0\npPhAY2MjKSkprompRMQlLuHy/PPPc9ttt/lcp6ioiIyMDMaPHx91d5g/VxiEF8C3slgaGxtJTk4O\neO4dX0TDFWZWINkT8BSWQC2W9evXM3ToUMaOHcvAgQPZvHlzNJvZafizWMAWFoBjgR3AiyLypYj8\nUkTSotyukPFlsRgtDLOUV3+uMON0tGAeY6msrAzJhePZA4bIucPWrFnjV+y2bNnC+PHjGT58eFSF\nxWoCLk/CqWWxslgiKSxmFks48bZIC1VnYgzeAwFnhi1fvpzp06cDMHXq1G7jDistLeXOO+8MeftA\nhKW3Z4YFMlZYrVLqBaXUKcAdwBygREReEZEjo97CIAnUYvHMCANri0W/ATwfJmausKqqqpCExRhf\n0YmUsKxbt86v62fz5s0cddRRDB8+nN27d4d9TCv06Xs9pwz2JFxXmFlWWKQtFtsVphGqK8woLNOm\nTWPFihVRa2MwLF68mDfffDPk7W2LJcAYi4j8wDm68dPAAuAI4H3g31FuX9AEGmOxslhqa2vZuXOn\nT4vFuD/PH39lZSWNjY1Bu7CiJSwtLS1s2bLFr7Bs2bKFo446ihEjRkTVYgnEDQbRdYWlpqaGJSxK\nKbeaIzC/F1577TW2bNkS0D57k7AE4gpTSrFixYpuabGsXLkyrLqarhSWTz75hCuuuCLi+w2WQFxh\n3wI/BB5XSk1USi1USpUqpd4CPopu84InGIvFTFiKiooQEdeEW8YbwKyX6tkr1ntqNTU1QbU7WsKi\nP9j8taezXGGBBO4hdFeBUspSWBoaGlwWSzjB+6amJuLj492GtDezXhctWsQXX3wR0D490417eozF\nn8WyY8cOkpOTGTJkCACjR4+mvr6+W8xTsmrVqqgLi2cZQ6R477332LlzZ8T3GywBxViUUlcppVZ5\nfqCUujEKbQqLQGMsZhPx9O/fn82bNzNy5EiXq8bTYvHnB6+srAQI2h0WLWFZt24dJ554os8HqVKK\nLVu2MG7cuKi7wqJtsbS2tiIipKamRi3G4hkPAetEjkA7GJFMN/7oo49Yvnx5SNuGQiiuMKMbDLRk\nle5gtdTU1PDdd9/R0NBAe3t7SPsI1GKJRoxl8eLF3WJInUCEZYCIvC8i5SJySETeFZHQ5oyNMu3t\n7Rw6dIgBAwaYfh6IK6y5udltDChjMVOgMRYITViiEbxfv349p556qs8H3L59+0hNTSUzM5OhQ4dS\nXFxMW1tbWMe1wl8Ni06owXtdPMwezJESFrNEi3CFJVKuMIfDwY033sgHH3wQ9LahYha893f/ewoL\ndA932JdffsmkSZPCGqKnq1xh+/btY9euXT1GWF4D/gEMAvKAN4HXo9moUKmsrKR///6WrpZAXGGA\nK3APuFKAGxoaAsoEqqqqQkSCdoUZh3PRiZSwTJ8+ndraWsuaGD2+ArhGGzhw4EBYx7UiGFdYKBZL\nIMISbozF0yUK3kKgd3IC/ZFHSljef/99vvvuu07LOHI4HFRVVZGZmelaFogrzEpYujqAv2rVKk45\n5RT69esXsjusq1xhn376KTNmzOgWo0sHIiwpSqlXlVJtztdfAd9nrYvwFV8B73Rjs6wwwM1igY7e\nRSCZQJWVleTl5XULV5hSig0bNjB58mRiYmIs02E3b97M+PHjXe+j6Q6LtitMF4+kpCSfWWHhxFg8\nXaLgfS9UVFTgcDiCcoVFIsayYMECLrvssk4TlsrKStLT093iTf5cYcXFxdTU1DBu3Di35ZMmTWLH\njh1dWpG+cuVKpk6dGnVhiYYrbPHixcycOdM1kV1XEoiw/EdE7hKR4SIyTETuAP4tIlkikhXtBgaD\nr3HCIDSLBTqExfOBkpiYSEtLi5svtqqqimHDhnULYdm9ezepqank5OSQlpZm+YM1WixAVDPDAnWF\nZWdnc/jw4aD93P4slpSUlE5xhemDkwYiLA6Hg6amprDTl7/88kuKi4u59tprO01YPOMr4N8Vtnz5\ncqZNm+aVcp6YmMjEiRNZvXp1VNrqD4fDwerVq5kyZUpY90hXuMKUUixevJizzjqL/v37B+0xiTSB\nCMvFwLXAUmAZcB0wC/ga+F/UWhYCBw8etAzcg/8YS3JyMiLiU1iMP34RITEx0W3AvcrKSoYPH94t\nssLWr1/PhAkTAHwOY+IpLNHMDAvUFRYfH09aWhqHDx8Oav+dEWOxCt4bLaTS0lJEJKAOhm4JGx+0\noQjLggULuPnmmxk0aFCnCYtxSmIdf66w5cuXc+qpp5p+NmLECIqLiyPaxkD55ptvyM/PJzs729Ji\nefHFF7ngggt83j+BCEtmZiY1NTURi2Vu3ryZfv36MWLECNLT07s8zhJIgeQIH69uFcQPxmIxywoT\nERYsWOBKgdSxEhbPfba1tVFfX09BQUG3CN6vX7+eiRMnApCWlmYqdnpGmKewdLUrDEIL4OvjNHV2\n8F53s+pxrIMHDzJ06NCAOhieqcYQvLDs2rWLZcuW8Ytf/KJTq7rLy8vdAvfg3xX2+eefWwqLZ0et\nM9HjK4ClsOzatYs1a9ZwxhlnuM3TZMTf6MYAsbGxZGRkBN1xsmLx4sWcccYZQOAjH0STQAok40Xk\nRhF5y/n6lYj4Ho+ji/CVagz+h3QBuOWWW9zGDwPrGIvnPqurq0lLSyMzMzNoYQk3eN/a2srvf/97\ntwD9unXr/FosxcXFJCcnk52d7VoWbVdYIBYLhBbA97RYjOdD/ywlJYWmpqaQ/dBm90FsbCxxcXGu\n61VaWsqoUaMCEhbPVGMIfqKvp556iquvvpr+/fuTnp7umvk02gTrCquoqKCoqMh1X3rSlcKix1fA\nWlhqa2u5++67KSwsZPr06aZ1N4FYLBBZd5hRWHqExQL8AZgE/N75muRc1u3wF7z3dIUFcvHBOsbi\nuU89O8bKOvBFuK6wPXv2cMMNN/DnP//ZtczoCrNqk6e1AtF1hQVrsYQqLHFxccTExLi5GvTPYmJi\nSElJCTk4a3YfgLuVUVpayujRowO2WDyFJdiJvv75z39y9dVXA5rlHa06CU/MhCU1NZX6+npT4V6x\nYgUnn3yyW7DfSGJiYkTnOAoGo8ViZdXqnYD58+dzxRVXcOGFF3qtE6iwRCozrLW1leXLl7smSusp\nwnKiUupKpdQS5+vnwInRblgotLe3k5+fb/m5v+C9FbprwZ8rrKqqioyMjJAubLjCUlZWxuDBg7n7\n7rs5cOAA5eXl1NTUMHz4cMDaYtHHCDMyZMgQSkpKojI1cqDBewjNFaaLB3jXGRk/C8cdZnYfgPu9\ncPDgQUaPHm15Hxh75WbCEowrrLm5mbKyMte1hs4b5NBMWGJiYkhNTTUVVV9uMNCumZXFMm/evKjV\nVx04cIDa2lrGjBkD+LZY9Gt1zTXXmLqMg7FYInGNvvrqK0aOHOm6DsHMhxMtAhEWh4i4otnO4shu\nOXHCokWLOPvssy0/D8QVZoavGItxn5WVlWRmZkZMWOLj44MSlkmTJnHttddyww03uKyVmBjtEvuy\nWIypxvpxBw0aFJXhNTrLFQbeD+dICYuZK8zzeKWlpa653M0y24YMGcLGjRuB8GMsxcXF5OXlublw\nozVkiCdmwXuw7jX7ExZfrrBHHnkkag/MVatWMWXKFFcCRSDCoounZ31YIPOxQORcYUY3GAQ/PXQ0\nCERYbgeWisgyEfkMWAL4ntzDgIicIyLbRGSHiHiNRS0iC0VknYisFZHtInLY8NmVzu22i0jYI6sZ\n3VZmwXsrfMVYzCyWtLS0LrFYcnNzue+++9i2bRvz5s1z82NbWSx79+5lxIgRXsuj5Q7rLFcY+BaW\ncIokA3WFDR48mH79+nkdp7GxkbKyMu666y7Ad4wlkIne9u3bZ5pw0hkWS3FxsVfwHswDyLW1tWzd\nupUTT7R2eFgJi1KKlpaWqA3MuWHDBiZNmuR6byUsxmuVkJBAbGysl+uus11h69atY/Lkya733d4V\nJiIxQCMwCrjR+RqjlFoayM6d2z8HnA2MBy4VkbHGdZRStzoHtzweeBZ427ltJvAAmtvtJGCOiKQH\n8d28CNUVFmiMpbKy0uUKC6XyPpysMF1YEhMTeemll1ixYoWXsJi16fDhw26Be51IZIYppbzcacG4\nwkJx5wRjsYRaiBeoK2zQoEGmnYzDhw+Tk5PD9u3bWbp0qakrLC4uzi0ZwBdFRUUMHTrUbVlnuMJW\nrlzJtm3bvCrowVxYVq1a5RouxQorYdGXRUtY6urqSE/veLxYWbSe1qVZh62zg/clJSVuIYBAhCXa\nk/n5FBalVDvwO6VUs1Jqo1Jqg1IqmJSNycC3Sqm9SqlW4A20kZKtuJSO4WLOBj5RSlUrpaqAT4Bz\ngji2F5EQFl+uMD1431UxFr3neMopp7Bo0SK3KZKtCiQPHz7sGsnZSCQslpUrV3oFN4NxhUXTYgnX\nFWZlsTQ1NdHe3k55eTkDBgww7WRUVlYyYMAAHn74Ye68805qamq8hMWs/VaYWSzRFpa2tjauu+46\nFi5cSFqa97x/Zr8Bf24wsA7eR1tYPH9/gbjCwNzF3NkxlgMHDpCXl+d6H0i68fbt28M+ri8CcYV9\nKiI/Fn8zM5mTD+wzvN/vXOaFiAwFhqO52sy2LbbaNlD03pA+332gWWHZ2dlUVFT4dYXpFktXusJ0\nLr/8crf3Vq4wK2GJRMpxSUkJBw8edFsW7ToWT2ExPqQ6K8ZSUVFBWlqaq8jT88Gjn/NLLrkEh8PB\nq6++6te15ouusFieffZZBg4cyMUXX2z6udnDLVBhMbNY9OsYLWEx3hsQuLCEY7FEwhXW3t7uVWYR\nSMc23Inu/GGe8+fOtcCtQJuINAECKKVUINMTm4mRldN4FvCW6nAqB7ztgw8+6Pq/sLCQwkmT4I03\noKwMrrwSnGZiTEwMCQkJNDc3B2WxJCQkkJKSQnFxsamwGNON8/PzXdaBUsrvTIk6+phLnscNRljM\ngqg6Zg84h8Ph5QLQiYQrrLq62uuYkQze//CHP+TZZ591e6g2Nja6etDRygqrqakxPWf68Yw/dCtX\nWFZWFjExMTz22GOceeaZ/OxnP/PaX6C1LEVFRW7WKURXWIqLi3nkkUdYtWqV5f3tKSyNjY2sXbuW\nKVOm+Ny3VVaYvixaqcieHbv+/ftbxliMnQDP35UeCwrkHo+EK6y8vJy0tDS341kJy7Jly1i2bBmg\nlSJEE7/CopTyttEDZz9g7EoVAFbD5s4CrvfYttBjW9PYjktY2tvh1VfhssugpERb9vjj8Npr8P3v\nAx29wGCEBbSbYNeuXT6zd/TgfVxcHMnJyaZBWTOUUpSUlLiZs4BLBAPB02LxxKxnVVVVRXp6uitz\nzEgkXGFVVVVewhKMxZKVlUV1dTVtbW2mdQ8bNmxg3759XsIS7eB9dXW1qbDoxzPWU1m5wnQr8Ywz\nzuCcc84xneoh0FqWSLjCNm7cyMcff8ztt9/ud91bbrmF66+/ntGjR1uu4/lw++qrrzj66KNNLTMj\nXWWxBOIKa21tpa2tzc0a8fxd6fd3IB1KM1dYa2sr8fGB15+XlJQwePBgt2VW6caFhYUUFhYC8Lvf\n/Y5333034OMESyCV958GssyCNcCRzsErE9DE4z2T/Y0BMpRSXxoWfwycKSLpzkD+mc5l5tTVwckn\nw89+1iEqAFVV8PDDmujQ0asMJisMcFkDgaQboxSvORy0Pfecdlw/Fd4VFRX069fPy3z2slh8iIzZ\n0BpGrFwyxuHOjRQUFHDw4MGw6gaqq6u9bvBggvexsbFkZmZaDp1RV1fn5W7pjOC9PsKCJ/rxjEML\nBXLe33//fS6//HLL/fkjHFdYW1sbjz76KKeffjpz5szx60LZt28fS5cu5e677/a5nqfFEogbDLou\neG8mLJ4dD90NZhQNz+sbqBsMvC2W3bt3k5+fH1Tiz4EDB7yEJZB042i7wiyFRUSSnKMX54hIpj6a\nsYgMR5uXxS9KKQfwK7TA+2bgDaXUVhGZKyJG230WWmDfuG0l8BDaQJergbnOIL45/ftDnkmzBgyA\nf/wDnL1y3XU1a/VqLrr5Zhg6FI48Ek44Ac44A95+23T3Y/r1Y3xsLAklJVBaCjU1UFdHP0OwUbdY\n+Otf+UFTE5n33AOnnAIjRsC2bTgcDtNqZJe10tgI333nWu4mLFu2wNFHw7/+Zdq+UCwWq/gKaFlJ\naWlprhkxg6KqClavZsT//kdhczPNhh9PMK4w8B3Ar6ur83oQdkbw3soVZhQWoyvMKsaiExcXZ9rD\nDURYqqurcTgc2n1nIBBh2bt3L1OnTmXp0qWsXbuWk08+2e98KJs2bWLChAl+6zSMwuJwOHjzzTfd\nai2s6M4Wi1n2nufvKhhh6devH+3t7S5354svvkhZWRn/+c9/Am63maejO8RYfFks16KNYDzW+Vd/\nvQv8LtADKKU+UkqNUUqNUkrNdy6bo5T6wLDOXKXUPSbb/sW53Wil1CK/B3viCfA0I//+d1eMBTp+\nrClNTSRXVsK+fbBzJ3z9NXz6qSYaJvxi3z42ORwwfDgMGgTp6ZCaysTdu90slmyHA26+uWPD1au1\nYyxYwLx583jyySc7PqupgeXLqf3sMy4XgbFjYeZMcFoJLmH59781a+y772D2bNiwwa1t9fX1KKU0\na0opzXorKtK+izPd1+oBd0JcnOt4nuhJC0Hz0ktw8sn84qOP+AiIP+oo+NvfwJk0EajFAtYB/NbW\nVpqbm8OyWEL5cSmlqKmp8WmxGF1hvmIs/ggkxqK7Aj2FyehGtOKll17imGOO4ZNPPmHIkCF873vf\n47PPPvN5PLOCWjOMD7fnn3+ezMxMzjrrLL/bdWVWmFEszWIsZtmA4Vgs+tA75eXltLa28vLLL3Pz\nzTfzL4vOoxlmFksglfddJixKqaeVUiOAXyuljjCMaHycUuq5qLYqVI48Em65BX7yE9i1C4qLwelT\n1HGNQmvlnvLo+emkeQxMqRMfH+8WY8nZvx/MAu6LFlGxaRM7duzoWLZxI5x6Kqf83/9x16ZNmhhs\n2gTO8b4SEhI08fvRj0DvFdXXw4QJmnXlRLdWRATefx9SU2HYME0AExJg8GBSExO9LBbHhg0sXLNG\ns6pefx08RloNWViGDXN7G1NWBpdfDmeeSWxtbVDC4grgt7Zq8bNf/xrWrXP9MPxZLFZZYaHGWOrq\n6khKSjKN+Zi5wqxiLFYuSLP9+cLMDQaBjZ576NAhjj/+eJcoFRYWBiQsnkMAmaFbLBUVFcyZM4dn\nnnkmoLiDVfC+M7LCImGxBDKysRFdWD788ENGjBjB3XffzccffxxwbNXMYklJSaGlpcVn4k9XWiwA\nKKWeFZFTROQyEblCf0W1VeHwm9/Am29q7icT15j+YxWrnpyJiwMg1eJHEe/sYSmlqKysJOXCC2Hz\nZtZ6jrIcG0vGt99SVFTUscxqEMT774eaGhISEiiLj4enn/Zex5DG6+YGMyl25OBBMp57zn34icpK\npi1YQFJbG6xZoyU85OZqlpFzHTdhaWyEl1+Gv/wFXnlFy7pbskQTQs/qcJMHHQBtbVSDb1dYe7tm\naTl/WC5XWFMTzJ0LCxbAiSei/qCNg+ppsWQdPsyk+fOhpsYt/qWUchtqwy3GsmyZl6haYeUGg9Bd\nYVYEErw3C9zr+HOHecblTjrpJDZv3uwz9mQ2tpwZurA88MADXHTRRRx33HF+t4GeEWMxEo7FAh0p\nxy+88AK//OUvGTBgAMcccwyffhpYGNvMYhERv+6waM/S6TcrTEReBUYC6+kYI0wB/l1TXYFJhpMR\nvRf72NChpL3wAidMnKg9NKurtdjA8cebbteWm8ve3bsZNmCAtn5TEyhFfGKiKxlARLQH19Ch/P7c\nc7kkIYEzGxpg3Di4+mremjaN2H2G0hwzYYmLg5/+FDC4wq69Fr75Bn5n8EAaXH5+hQWImz+fs2Nj\nO+p3bruNDM9Ux/Z2zdJziqg+iyMA5eXwi19477h//w5rSmf4cDj2WFbu3s2k2lptHuv4ePjDH2ie\nN8/bYlm4UEu42LwZvvhCuw7JyfDdd2RlZWltSE3VrKpTToG2NrLuvZfHgZ1GYXnjDV5Yu5b+7e3w\ny1+SPGaMW0FsfHy8KwMuq7WV3JIS+OMf4Ve/glNPhY8+0iw8M9rbISbGMiMMtHururo6oq6wUC0W\n8C8snnG5pKQkJk2axMqVKznnHO9aZLO5e6xIT09nx44d7N+/n61bt/pdX6c7x1isXGGhxlhAs1i+\n/vprvvzyS958800AZs6cyb/+9S/OPfdcv9ubWSzQ4Yq0irt2ucUCnABMVUpdr5T6P+frxqi2Koq4\nitgcDqSgQOtdjxkDkyfDWWeBRS3ItltuYfbkyZq7qqxMe5jW1VE6dSqNjY2u4kid9IwMNowcqfXu\n77oLcnI4cOAARUVFHVZDWhqccgq70tOpHjIEfv5zLUi/cCGkpbkH7598Ei69tKNBhgegl7AkJmrW\nWlaWSySIiWFqfHzHj2D+fHYUFHh/UcON6GaxWFlXZtMUDBgAGzZweXY2M0eOpOy44+COO2DcOO/g\nvcOhWSJPPAH/+Y8mKvp+Bw92/+EOGaK5wpz8GpiqT2P72mtw6aWaqAD8/e88OG8eSU7LztOHPvLT\nT/njF1/AdddpbVi6FK6/3t362rZNa9vRR2uCn57O8PPPZ4bF+F2BusIiJiwOB0VFRSFbLGYJH77i\nLHodV5afzhtoFktdXR1z584N6LvqdBeLJTk5mZaWFrdkGytXWDgWS05ODk8//TSXXXaZ6/gzZ87k\nvffeC2i+ILN0Y/AfZ+kOwrIJsJ49q4ehu0eCrWOZNm0at9xyi9dy3QLSh3PR8eyp1tXV0dbWRkxM\nTIf75owzYOVKLhs7li1//7sWWxk1yrWNm7DEx2sPz7Iy2LFDc0U5cXtA5ORoFlVxMVRUaLGJ8nKo\nruaFQYM6fgQDBvBwYSH/uftuuPNOOOaYju2dBCQsJvUXOtXV1bSPHMnHt90Gzlojr+D9xo1aEoMn\nZ50FIu4/3DVr4KmnXKu0A+319Zo18aMfwbHHeu0m3Zl67lZZrRSDPvnE+5h//rOWxAFaQse4cVq7\nN2/WBKemhpTdu0m2qMWYvGQJp65ezY8PHWLgf/8Lb79Nwfbt1jGW996Dd991JVh4Yhq8r6+HZ57R\n7pOUFK7+6COGWUwVEYqw+IqzbP3mGz5taIDMTDjzTO0esyArK4sFCxZwzTXXWK5jhj+LJRoFkvp4\ndsb6EREhJSXF7fxbucLCsVhyc3MpLS3ll7/8pWvZ8OHDycvLY+XKlT63VUpx8OBBU2Hxl3LcHSrv\nc4AtIvIV4LriSqkfRK1VUUQXgmCGdAHtBpg5c6bp/hobGztSjZ2kp6e7Va7rJmtSUhL79u1zEyHP\nsRpnXwQAACAASURBVH50TCvvc3K8rCq3B4RnLCg21uUe8ww0Hq6spO3ii+GCC2D+fO1BYbgZs7Oz\nO2JCaWlajZBS2oO8oQEOHTJ9mENH9tSQIUOoqa3VevyY1LEsX266Pc4MIrcf7gUXwIoV2t+SEmKA\nK777Dq6+WhOFf/yD+nHj6GewKNKd7j43YVm1ikSjSxKgXz9NuE84QXs/cqQmtt9849W0Jgur9vh3\n3+VEh4NLQLM+geETJlBteBg6HA5qa2tJT0uDu+/WLNQBA2DGDK3zcPzxrqxCtxjLpk3w+99rqfOG\nhIrGhgaONBmdmrVrycvIsBQWh8NBVVWV1wCkJ598Mhs3bjQdF2/Ttm0cnDyZoxYvhsWLtXjcBx+A\nSfwkJiaGW2+91fTYvnBlhVVWwqJFmov0zjtpbm5GRKJisXgO56Kjx1l0MYmGxZKbm8tJJ53EMXrH\nzonuDvNV+2NV/wb+U467g7A8GNUWdDKhVt5boVtAXq4wjwuri0f//v0pKiriWOcDub293TUSrieB\nDulSVlbGKIOlY4VnoNHLJZOf75aa7WaxjBqlBe8DpL6+nsTERHJyctyO6eUKKyzUrIKKCs3lNHWq\nZik4xcFrVOZJk2D1aioKC8netYvK2Fgyr7pK+2zMGG5KTuaFpibE6RLLctbhuD084uNpPu004pYu\nJRa0LLZ//QsmTnT/EpdcYiosbWbuv9ZWYk1cF0k7dlBniMlUVVWRlpZG7Nq1mqiAJtC6BVpd7RIW\nPWYDaMLyB++JWx9oaeFzT5emUnDCCdwHHEpP19LT4+M1S+OPfwS0a5+enq7N4VJaCi++CI2NpLS0\n8FJaGod/+lP6HXMMPPSQa7dbtmxh0o9/rCXGvPAC7N+vZSj+9rcQQMV+ICSXlPBIba3m9qyv19y5\nDz9MU1MT6enpUREWs3H6wDvOEo0Yy6WXXsr3naOCGJk5cyYXXHABCxcuRFav1oamOnQIHnlEc9li\n3SGFrneFWQqLiIxVSm1TSn0mIonGUY1F5OSotiqKhFp5b0WgrjA9eyMzM9MtM0wfsNCsLcEIi6/i\nSB0vi8WPrz8rKyu0dGM6hovxPA9errBjj7W0esBiVOYhQ3jr9tvZ8PrrLNm+nW3OecoB/upw8Nyy\nZSRt3MgXu3bxwaZNfB8PYZk8mbb332dMTg57lizRrASze+Hii7WMsYsvBucozYvmzyfFLMZi8cCL\naWjA6Cx0nXMrkTZYnMnJyR2DeJoM8+HIyaFIxPtB5kwsiQEGVVV1FNUOHuwSFrd7pqIC7rvPtfkl\noLno1q71Epabhw3zTqdfvlyLfXlay01N8OGHWgwrIUGziHft0uJpnusuXQrz5pH8+edc397e4Xo9\n/3yIi6O5uZmMjIwOYVmzRhtR48wzNet21CjvfQaIlbB41rLU1taaTsPsabEEMsmXTnp6umkyyNFH\nH01cXBzb3n6bcVddpXU4AG64Qat3mzHDMr6i77e7usJeA/QUqS8M/wP83uN9j8HoCouUsFhZLMYb\nTu9d5OTksM/ghvHV64i0sPi1WDwIuY6FjvG00tLSKDb44oMZ0gWs55Gpq6/HMXYsRWvWuJa1t7dr\nwjV1KkyfzuEPP2S3MyPJ092RkpLCvpYWHJMnu8286MaoUfDf/7ot+q5fP9LMgtexsey58kr++cor\nTBg5ktMnT4a6OlRWFg2LFuFwOIiNje2Irzjnp+f11zseGuAlLC4fv/GcZWTADTfwzYQJFMyf790W\nq3iYAbd7xmSkZk+UUmzevJmBb7+t9ZqNrF6tuUc9z+OePTBrlncB7q23ulnGgCZWy5Z5jzzrFPSm\npiZ3YVm6VItRveccIWroUJgyRSsgPu887y+wfr0mdOnpUFCgZRk6sRKWkXFx9Hv9dc0lN2QIcugQ\nqR6doLAslsZGzVKtrISzzwZD4amI8IOTTiL/2mu97481a2DGDJ/PDl+uMKVUlwqLWPxv9r7HEC1X\nmKfFYuUKGzRoEB999JHXcjMCFRZ/44TpGC0Wve7GV6FeuMJiNulZsEO6WM0jU1dXx8CBA2ltbXWJ\nlW6FxhiG79EfRJ7CIiIuV4dZFb0VNTU1DPMoAAWgXz8OXX89v37lFW6+4AJOd46wIEDJO+9QW1vr\nKljMysrSXHqTJmkZgEuXagkWSrnVALnFWEaPhnvv1WI/P/kJpKby3VtvmacaOxzaejt3ui83WFqB\nCEu70+oBLUaYkJBAzsCBWvLAkUdqLjulNBfNF1/AtGnuO7j1VvNRHXbs8BaW007TBNPYy87K0l7O\nERsyMzM7zodnYLuoSHtNnmwuLDffDMakhIwMzd32yis0tLZ6C8spp/D22rWa1ebkCRHWZmSA7nrF\nMD3xQw8hM2bQ1NDgX1iU0mrt7rgD9u7Vlt12m2Yl/sAZum5r4/YVK0jz/P3NmaMl22CSEdberp2X\n0lIGJibyncXIyc3NzcTFxQWUdRYqvoRFWfxv9r7HkJycTGlpKa2trUH1nH3tT7dYfM2JUFJSwvHH\nH09+fr6bK8yswEknGq4w/SFfW1tLUlKSz3OgC0sww//rGC0Wn66wINpspK6uzjWRlp6v7ykevoQF\nOookgxEWf3UsgCvVWEe3FN2EpWMjsKhXcEs3HjNGc/0YsKxhGTgQvvuOA9u28f+mTuU9vf7J8P3d\n7hk9kSA5WbOM4uN5+ne/Y/p557ncEl5Dudx4o+bC/MMftNiNZwfl3//W0sfN+PZbTUiMJCRoD9VF\ni1gfG8uRTz9N/2uucVlqusVSWlqqPZhXrTLft9W0x56dk6oq7eVwmAfvPeYRAohTiiaPRIX4+HiO\niY1FHngAHniAW4CmhAR46y0t2eKCC9x34nDAo4/CAw+4L09JgdNPNxwsjrKLLiJn4ULi9Q7BlVe6\nbXfgwAFthOnaWs2qevZZcE7gddwVV/C1hSWuz4IZaHV/KPgSlgIReQat06X/j/N9WBNudSVJSUlU\nV1eTkJBgOlx8sBhjLOPGjXMtN4ux5OXlMXToUDdXmFWBEwQmLM3NzTQ2Nlo+7IwYe/+B1FKkpKQg\nIjQ0NJhObOULY4wlGhZLbW0tRxxxhCtIaSUsxsI6M2EJ1iVgNbKxfjzAKxHDeA6MQ+b7w18di68a\nFoDsESP4T00N6pJLvDoGbsKSkKA97AzkDRnCr558kpXOToVpYWRhodeQSS4SE+H//T9t3LqmJu2V\nlaVZXob52d247z64/36+P306X8+cSX9DB0S3WFxTOfz3v1pm2iefaDGepiatONqiwNlLWHRSU2ko\nK/O2WEysjg1paYjJOGk/jYtzizsltbRoiQ3GUTZ0YmPN43kXXOBlOWbceCOzXn6Zf7a3w0knaQkT\nhutYUlLC/Lfegptuct/XwIEcOvtsqt56y3357t2wYgXtTU2cHhfHP7xbETF8CYsxzeN/Hp95vu8x\nJCcnU1VVFRE3mL4/q3RjzxjL4MGDyc/P58CBAy6f+4EDBywH9QtEWMrLy8nJyQnIokhNTXXFOwId\nr0q3WoIVFqPFYjwPwVosiYmJrhk/jddMz9AxBin9WSyeD49QhMXXkC66C8TMYtE7Gb6mKvDE3yCU\n+/btY6ohccGTxMREUlJSvNy04D+T8Ec/+hH33HMPK1asYPr06WzevNmVyRgQp5/u3gMPBGd7zGpZ\nmpqayMzM1DoKIlo22oQJWtJAY6MWQ/n2W+t40cSJmvurokJ76Ou/q9RU8xhLcjIOEUqPPJK8H/wA\nPviAvzc0cLHn/EpKMdOiDslymKBvv/VedtllXouGDBnCJy0t1CxZQtqYMV4JHAcOHCDBzNV4882k\n5uZ6x1g+/hiuu46BwN8hqsLiaxDKV3y9otimqBJpYbFKN05KSnIFk5VSLoslMTGR7OxsSpyFe/5i\nLK1WN60TfzNHGjE+5AOt/g41zmKMsRhv8GCD92ButejCYkyr9BQW41hhkbRYgnWFGTsZgZ53fX/h\nWCxgXSTp776JjY3l9ttvZ74zOSDQoVwigZmweGWFGUlO1gL3V/gYwvDvf4evvtLiTo2NWn3MmjWQ\nm2suLC+9xH3XX8/r116rZbFt3cqbcXHeE5UpxXMFBRw++2zNIjNi9bvRheXcc2HrVs219wPvskAR\nYezYsWxua9PclR6UlJQgZhloEyaYpxt7xtyiSPi+oB5GUlJSRIUlLi6OmJgYysrK3HqFIuLqqeoP\nRb24yugOCzd4H2h8RT9+MK4wCE9YIuEKA/M4i164FozF4iksoYxwHK4rLBhh8TcIpa9xwnR8CYu/\n++aKK65g3bp1bNy4kc2bNwc0XH4ksLJYLIUlWGJitJG/TzgB4uPNhWXCBOIyMzvSjUWoNpsRNiaG\ntUOGsPGuu6CigmuvuoqXFyzQ3E73eM0EonHXXVp91IcfaqnDPjjqqKPYotc7GdBnnY3VLbS8PG3w\n2uJiOOcc83TjThSWQAokexWRtlj0fZaUlHhNtqT3VCsrK8nLy3O5q4YMGUJRURFTpkwJO3gfjLB0\npsVSVVVFXl5e2K4wMLdY9ACkL4sl0OB9MPhzhQ0YMMDrehhdYYG6ID3b74ke1zMrrDUSjrAkJSVx\n0003cdtttxETExPwfRYuVhaLW1aYAYfDwd69ezniiP/f3plHx1Gee/p5tbRaki15wbJjLcZGNl6x\nYMALZMAJJECcA8OZhMSBCcm5l7BMEriHEEgy3OCTc5LLvWeSMJMQQoBLCBmYITcBBi4BgmMHhs0h\neF/B2FjeZMu2LC+ytXzzR1W1SqWq7upV1db7nOPj7urq2lT9/er3vt/3flMy2l/QyPsRI0YMmHbA\nb+Q9DHxgO3HqFCVjx1qFWIPwKfAZRJCwHDx4kKqqKkrefNOqajFixID8i29344ULobKSvZs3c/DD\nD0NX9M6EYedYnNHM6YyOTUU8Hqe9vX1Qg+H8cb3i0dTUxEcffURfXx9tbW2BjcPp4Fic6+yEBDPp\njRfkWMLmWIwxBQmFlZSUsGvXrkFzlmcTCgvKsbS2tjJx4sSUHVCyERaAm2++mXfeeYeZM2em3TMw\nU/wm+0rmWFasWEFzczO33357RuXgw4y87+np4dSpU74C5H54SnfkfSpmzpzpWx06EekYPdoak+P5\n2zhtj3EP5r3zTvjtb3nlttv4UYjKydkQZs77fxaRGhEpF5FXRWS/iAyeoLtIqKys5NChQzl3LMCg\nBsd5UvWGu5xQ2P79+xk1alRgQ1tWVkZPTw99TsVeH6LgWJ588smBNzD9ORZnvx0dHXR3d1NeXp52\nA5VpjsUJU/b09OREWE6dOkV3d3fSkdV+E4BlGgpL5li2bdvGZL8aYR78hMUYk+j0kYra2lpuu+02\nLgjqxpsH/Cb7OnnyJDU1NfT09Awaf9HR0cHFF1/M4cOHmT17Ni+88EJa+0smLM794dxvfveu+/7M\ntbDMmDHD17EkG3UP1jUsKSnxLdrpV5om14RxLJ82xhwBPgtsB5oZ2GOsqIjH4/T09ORcWGpqagaN\n4HaeVL3C4oTCkuVXwMrTpErgD7VjWblyJV/60pf6S4/YuJ/snYY1kzCYc9yZ5Figv3HORY7FCYNl\nIozuXmG5yLGEnXDLT1gOHz5MVVVV6N/A0qVLue+++0KtmwuCcizxeNxXbDs7O2lsbOSxxx7j0Ucf\n5frrrx/QpT8VYRxLUBgMBt6fuRaWyZMn09bWNug+TdV2QHBZl6gIi+PrPwM8bYxJPplyxHEal1wK\nSzweH5RfgYGhMD/Hkiy/4pAqHDbUwvILuzDiLk8JdWccC/QLbCaJe8g8xwL9PcNykWNJFgZLhnP+\nYaoduHGe3P0ca9i55/2EJZ17BqwHHD8nli+CciwVFRW+wuKuQHzppZdywQUXsG7dutD7C1MrLJmw\n5NOxlJaWMm3aNDbbAx8dUjkWCC5EGRVh+b8isglrwq9XRWQckPtJEQpEPoSlsrIyqbB4B0E6OZZk\ngyMdciks3oF66Yxj8ePQoUP8/ve/Z/78+YOExetYOjo6MnYsfj3LjDHEYrGsHEu6obAjR46kNUrf\ne/zHjx+ntLQ0dMNTUlKSKFXjJaxjOeOMM7IWlkKTrmPxNpRBCe8gvJPAObgdS7LGOJ+OBfzDYWEe\nSoPqhTkPZfkkzJz3dwMLgfONMd3AMeDqvB5VHnH+6LkWFr9G2p1jcd8E48aNo7Ozk/fffz9tYXnt\ntdcGJHTD1gmD/nM+efJkThzLY489xuLFi2lpafEVFneOxXEsmYbC3M7CHe9O5VhyKSyZOhbnPkgn\nDObg15A6UwQXyrEUmlSOxSu0fsKyfv360PvzGzwLA3MsQ+VYwF8owzyUBglLJByLiHwe6DHG9IrI\nfwOeAJKfUYRxGpdc9woLcix+OZaSkhIaGhp455130haWm266iZ+6ZlFMt5FwGvlshcUYw4MPPsgt\nt9xCfX39AGHp6+sbEJ5wC0umoTC3Y3FvO5eO5fDhw+zcuZPOzs5BnREg+1BYJsLS1NTEB57xB7t3\n707MdZOK5uZm3n///QHhtKgLSzwe9+0VFo/HBwx6dfA+gWfiWKKaYwH/8wnrWKKcY7nHGNMpIh8H\nLgMeAQbPOFQk5CsU5udYgrobg5XAX7lyZdo5ll27dvGTn/yEzs5Oent76ejoSKuxcp7+sxWWZcuW\nEYvFuOiii2hoaKC1tTXx2ZEjR6iurk50ZnCuQzbJe7djcTckYRxLV1dXyuR9a2srs2bNYuHChUyc\nOJHy8nK+6xnglm0oLJ38isOCBQt46623BiwLGwYDGD16NKNHj2bbtm2JZVEXlkxyLH7C4vdw4Eeu\ncyzpzMcSBr8ux2EcS9RzLE7fvsXAQ8aYF4DsywIPEWVlZZSWlhYkx1JTU8NHH31EWVnZoJuyqamJ\no0ePpuVYjh49yqlTp7j00kt54IEHaG9vZ9SoUcHzifhQU1NDW1sbvb29vj8mL6NGjaKzs5MeT02i\nBx54gFtvvRURGeRYvE/2+XAsbmFxnsr8ftSpHEtnZycnTpzgmmuu4Zvf/Catra10dnby7LPP8jdX\nyXS/80r3+DNxLH7CEjYM5jB37lxWr16deF+MwpIqx+L+fY0ZM4bq6upB4dkgwjiWocyxNDc389FH\nHyVc3P79+wPnuncT6VAYsEtEfglcC/y7iFSE/F5kqayszHmvsCDHsmnTJt8bwCnFkY6w7N69m/r6\neu655x5+/OMfs3379rQbiJEjR7Jjxw7GjBkTqttsaWkptbW1HLKn+AXLNS1btozrr7eGM/kJi1to\n85VjcW87aBBkmF5hX/va12hububb3/524jNvFWrnvAqdY8nWscBgYUknLzcUeIXFXYQ0jGOB9PIs\nyZL3UcixlJeXM2XKFLZs2cKxY8dYvHgxd9xxR8oHw6iHwq4FXgKuMMYcBsZQxONYIPfCMnnyZM46\n66xBy2tra9mxY4eveDQ2NiIigwoWevEKy8SJE5k1axYXX3wxP/jBD9JuIGpqati+fXtaDZw3HPbi\niy+yePHixA/NKyzursbQn2PIVa8w9xNqWVkZlZWVHD16NGmOxa/xGDFiBOvWrWP9+vU88sgjA4TW\n6bnnDqdkGgqrrq7m5MmT7N+/P21hOfvsszl48CBtbW2JZZk4llWrViXeF5tjOXXqFOXl5ZSUlASO\nY/ETlrB5lqDkfSwWo6+vj+7u7iHNsYB1PmvWrOHaa69l9uzZ/MA1ZXQQyUJhQeeSK8L0CjsOfABc\nLiJfB+qMMS/n9ajyTK6F5Xvf+x7XXXfdoOVOI+QnLE1NTdTV1Q0q/+HFLSy7du2i3p5575577uH5\n55/PyrGExSssf/3rX5k/f37ifW1tLb29vYkfl18orKOjI+NQWLIci7P/jo6OtJP348aN47zzzuOZ\nZ54Z1LA4AyHdP8xMHYuIJK57ujmWkpIS5s+fz9tvvw30TxGcjrC0tLQUVSjMO/LePWVCWMcya9as\n0MISFAoTkUSeJdlTvuNYent76enpSfmbzoQZM2Zw++23Y4zhl7/8ZahoQ6RDYSJyG/BboM7+94SI\nfCPsDkTkChHZJCJbROSugHWuFZH1IrJWRJ5wLb/PXrZGRK4Nu89UOL1L8o3TCPkJyznnnMM111yT\ncht+jsX9/UyFJZ0Gzk9Yzj///MR7EaGhoSHhWoJyLLl0LO4fhpNnSVdYampqePfddwMrBDc2Ng4I\nh2UqLM6+Pvzww7QdCwwMhyWmCA45VQLAlClTOHjwYCKcGXVh8dYKc7uAMDkWSM+xBAkL9OdZUjmW\nzs7OxHHmo6bahRdeyKxZs3j66adDC1ekhQX4O2C+MeYfjTH/CCwAbgyzcREpAX4GXA7MApaIyHTP\nOs3AXcBCY8wc4HZ7+WeAFuAce593ikhOrkauHUsQyYRl4sSJiVHryQhyLGAl0N15gTDU1NRk5Fic\nKq8nT55kw4YNzPVM0eoOh3lzLO6R97nOsTjbz0RYUuGU3nHINBTmHGO6IUgHt7BkUr6+pKSEOXPm\nsGbNGowxRSEsyRxLqnEs0J9jCdMzLJWwHD16NKmwlJWVEYvFOHjwYN4eWK+88kpWrFiR1oR77o4t\nbqIiLEJ/zzDs12EleR6w1Rizwx5c+RSDB1feCPzcrkeGMeaAvXwmsMJYHAdWA+HrTSehUMLi3Iip\nem8kwy957zBhwoRQhQi9x5RNKGzt2rVMnTp10A/RLSzeHEsuQmFBORbojyWfOHFi0A+7srKSzs5O\nRCTtEEWuHUu6191h3rx5rFy5kt7e3rQT9w4tLS2sWrWKY8eOUVJSEqpH4FDhFRa3YwkzjgWsigOx\nWCwxoV4QTpHXoHsjjGMB6x5ta2srSCQkLH6OxbmumTzgpUMYYflX4G0RuVdE7gXewhrLEoZ6wN21\nptVe5mYacLaIvC4ib4jI5fby1cCVIlIpImcAnwCST5cXkng8XhBhKSkpYeTIkSl7fiXD61iy2RZY\nDdzRo0czFhZvGMzB61hyGQqrqKhARBI/Cr8cS5BjicfjHDx4MKOxBd6eYdkKizOnSLqMHTuWCRMm\nsGHDhrQT9w5Oz7CouxVI7VjcwmKMCXwCD5Nnce6ZoPBVmBwLWH/f/fv3R15YCuFWIMREX8aYH4vI\ncuDjWE7lq8aY90Ju3++v5fWmZVgVky8GmoDXRGSWMeYVEbkAeANos//3meAZ7r333sTrRYsWsWjR\noqQHVSjHAlbIa9KkSRl/P5ljyQTnqStdYXFCQsmEZdOmTYDVALsnXcp2HIt7G+PGjfPNsSRL3n/4\n4YcZCUtjYyPLli1LvM82FAbpXXc3CxYs4O2332b9+vW+HUVSMXfuXB566KGiFBZvjsXdWHZ1dRGL\nxXyLZDp5lssuuyxwX8nCYBDesTjjw6ImLO5Q2PLly3nmmWfo7e0d0Gbmg6TCYudINhhjpgN/S7Zu\nAK1YYuHQAOz2WedNY0wfsF1ENgNTgXeNMT8Efmgfy2+BrX47SfciFVJYVq1aldXN5giLMxVpNmE1\n6O+plo1juemmmwatU19fz6uvvgoE9wrL1LFAf57FT1hS5VgydSzeHEu2jgWyE5Y333wz47nn58yZ\nw8aNG9mzZ0/khcVb0sXrWNxTNCR7Ane66CYjjLCkyrGAdX9G0bE45YlEhEWLFjFu3Dhefvll7r33\nXpYuXZq3fScNhdmN/WYRST6xdjArgWYRmSQiMeCLwHOedZ4BPglgh7ymAttEpERExtjLzwHmADnp\n5nznnXfyqU99KhebSkm2N5ojLO3t7VRXV2ddLiITxzJmzBja29s5ceIEW7Zs4Zxzzhm0jrusizfH\nUlFRQWlpKR0dHRkLi7tnWLIcS66FxQmF9fX10dnZmbFjqampSYRGM2HBggU8//zzlJeXZyQM1dXV\nNDY28vrrr0deWFI5FncoLJWwpAqFhXUsYUJhUXMsZWVlVFVVJe1Rmbd9h1hnNLBeRN7BqmwMgDHm\nqlRftAtXfh1LEEqAR4wxG0VkKbDSGPO8MeYlEfm0iKzHCnV9yxhzyB7h/5qIGOAIcJ0tdFmzYMGC\nXGymIDjCkov8CmQeCmtvb2f16tXMmDHD1+0ly7GA9cM7cOBAxsLo7hnml2PZtm1bXoRl165d9PX1\ncezYMSorK9Mqn+OmtraW0aNHp5xKOIg5c+bQ2dnJvHnzMvo+WOGwV155JWloKAqkk2NJVgLe3TMs\nKIeSSlicHEsxJu/BCsW3trYmfo9REpZ7stmBMeaPwNmeZd/3vL8DuMOz7CRWF+VhjSMsucivQH9I\nJpNxLEH5FYDx48fT3t5Od3f3oO7GYDWs+/fvp7m5OePjdjuWdHIshw4dGpDzCUtlZWUixNHd3Z1x\nGMw5/kzDYGCV9Tj//PMzStw7zJ07l6effpolS5ZkvI1CkCvHUldXR0lJCW1tbYEVLoLKuThUV1dz\n5MgRurq6knb1rampYefOnZETFqcDinPfFGLUPSQJhYlIs4hcZIxZ4f6H1d24Neh7Sm6JkmNZuXJl\noLCUlZUxbtw49u7dG+hY9u/fn3WOBfzHsRw4cABjzKBuo9n0CoP+PEs2iXvIXlgAlixZwuWXX556\nxQBaWloAii4UlsyxJGsoRSRlzbCgci4O1dXV7Nu3j+rq6qQDH6PqWLxd5gvlWJL58p9ihaC8dNif\nKQXALSy5ciwiktbTd1VVFSLCX/7yl0Bhgf5wmDfH4uz3wIEDWfcKA/8cy549e3y7jVZWVtLd3Z2V\nsOzcuTOrxD1YQp7OaHk/brnlFq66KmUEOhBnUGvUhcVb0sXrWNyJ/VQNZao8S5gcy969e1M2xlHs\nbgyDO6BEQVjGG2PWehfay87M2xEpA3CHwnLhWMaMGcP999+fdqx/7Nix7NmzJ2kopqGhge3bt9PV\n1TXo5nVCYblwLH45lr179/qKh7MsU2FxQgnZCsuVV17Jww8/nPH3c0F9fT1jxoyJvLB4S7q4HYt3\ngGSqaXbPPvvsQfPFuwmTY9m3b1/K8JE6loEka10GTzDST25nslECybVjKS0t5RvfCF3qLcHYKbII\nNAAAE3VJREFUsWOZO3duUmGor69n48aNiQKObpwnumwdi9+AuFGjRgWGu7IVFueHmW0orLy8nAkT\nJmT8/VwgIjz66KOJkFhUyVWOBSxh2bJlS+DnYRzLnj17UgpLTU1NynzNUOAd5FuI+e4hubD8VUQG\n1QQTkb8D3s3fISlucp28z5SxY8cmDYOBJSzr16/3fbJ3Rp5n41icQZYiMmA7zv6SCUumJUycUEK2\njiUqXH311ZF7qvaSqxwLwLRp01I6llTJ+zChMOcYonZth8qxJOsVdjvwBxG5jn4hOR9r9sjUZXmV\nnJDr5H2mNDQ0cOGFFyZdp76+nscffzxQWCDzGkVOaXK/hqSqqioxL4uXXDmW00VYioFcOpYzzzyT\nvXv3BhYhDZO8P3z4cCjHAtEVFqfL9dGjR33njso1gcJijNkHXCginwBm24tfMMYsC/qOknvKy8s5\nduwYhw4doq6ubsiO4+GHH/Ytm+Gmvr6erVu3+gqQ0yhnGgpzHIuflXc6I/g1HO4GKROcUEK2oTAl\nPBUVFYlqE06NOKerr984lmQlk8rKypg8eTIffPABs2fPHvR5mBwLECrHAtETlurqaqqqqhKzhkYh\nxwKAMebPxpj/af9TUSkwsViMHTt2UFdXl/HgvFwdR6qEf0NDAz09PYPGsEBuHYvfD2PUqFF5cSwT\nJ06kra2N9vZ2dSwFoqSkhLKyMrq7u4HsHAskT+CHybFAamGJqmOBgeGwyAiLMrTEYjG2b98+pPmV\nsDjHmI9QmONYgmLq+RKWsrIy6urqEp0SlMLg7hmWTY4FrDxLUAI/rLAUa44FVFgUH2KxGK2trUOa\nXwlLdXU1tbW1vg1wtqGwVI4lKBRWVlYWmH8JS2NjI+vWrdNQWAFx51ncjsUZANvTYxU6D9NQJkvg\np0rehw2FRd2xOGNZhnzkvRINYrEYfX19ReFYwHIt+XQsQd0lgxwLWE+52QrL/v371bEUELewuB0L\nDHQtYbrPJutyHCZ5D8WbY4GBXY7VsShAf0NcDI4FLGFJlmMptGOB7IWlqakpsQ+lMAQ5Fhg4SDKs\nY8k2FJZKWEpLS6mqqoqksGgoTBmEIyzF4lhmzJhBY+PgiT6dRjkXjiWdHAtYDVG2jgXQUFgBcZd1\nSeZYwjSUdXV19PT0JOYUcpNKWJz57MM0xjU1NSosNmGqGytDSLEJy/333++7PNtQWCwWo7S0lPb2\ndt8fxtixYwfNhe6Qi1AYqGMpJMkci1dYUrkJEUm4loULFw74LJWwgJVnCZOXGDlyZGSFxcmxFGrk\nvQpLxCm2UFgQzg8zm5k7R44cye7du31Lo9x222309flP16PCUnwE9QqD9HMs0N/lOBNhqa6uDiUs\nUXUs9fX17N27l66uLnp7ewsye66GwiJOsTmWIGKxGPF4PGPHAtYPd/fu3b4NyYgRIwJDVTfeeGNG\n0/k6NDU1UV5eHslG43QljGMxxnDs2LGk86Q4BOVZgkbku6murg4lXg8++CDz589PuV6hicVinHHG\nGWzdupURI0YkLf+fK1RYIk4sFqOqquq0iO9ff/31Wc1J4jiWdLtL3nrrrVmVrB8/fjyvv/56QX6Q\nikWYXmEnTpwgFoulrAgBwcISxrE8+OCDiSkHknH++edn9eCUTxobG9mwYUNBwmCgobDIM378eD73\nuc+dFo3ar371q6y+X1NTw7Zt2wr243CTzZTASvq4k/ddXV2+wpLOmIyg0fdhhOWSSy5J48ijSVNT\nExs3bizYb0cdS8QZPXo0v/71r4f6MCLByJEjQ1WaVYofr2PxhsK6urrSSkRPnTqVDz74YFAeLoyw\nnA40NjaqsCiKHzU1NfT29qqwDAPcyXuvY3HGsaTTdba6upqxY8cOmE2xp6eHnp6eyIavcokjLIUY\ndQ8qLEoR4fwoCvXjUIaOVI4lXWGBwXkWJ3F/OoSZU9HY2MjmzZvVsSiKF6cDgzqW0x9vr7Bscyww\nuLRLqnIupxNNTU2cOnVKhUVRvDiNiArL6Y8jLMaYwF5hmTgWdwJ/uORXoH8slgqLonhQxzJ8cHqF\ndXd3J+ZncXCEJd1R5MNZWMaPH095ebkKi6J4UccyfHAcize/Apk7llmzZrFu3brE++EkLCUlJdTX\n16uwKIqXmpoaKioqEnNyKKcvTq8wb34FMheWpqYmurq62LdvH5B6LpbTjaamJhUWRfEycuRIdSvD\nhFSOpaurK+3kvYjQ0tLC6tWrgeGVvAdLWLS7saJ4qKmpUWEZJjjCksyxZFKpt6WlhVWrVgHDKxQG\n8MMf/pDrrruuIPvKu7CIyBUisklEtojIXQHrXCsi60VkrYg84Vp+n4issz/7ab6PVYk2H/vYx5g0\nadJQH4ZSAJzkvZ9jyWSApMNwFpbGxsaCVejOq7CISAnwM+ByYBawRESme9ZpBu4CFhpj5gC328sX\nAhcaY2YDs4F5InJxPo9XiTZTpkxhxYoVQ30YSgEI41hUWKJLvotQzgO2GmN2AIjIU8DVwCbXOjcC\nPzfGHAEwxhywlxsgLiJxLAEsA/bl+XgVRYkAYXqFiUjaOYPp06ezfft2jh8/PuyS94Uk36GwemCn\n632rvczNNOBsEXldRN4QkcsBjDFvAcuBPcAu4CVjzODypIqinHaE6RWWSY4lFosxffp01q1bN+yS\n94Uk347FrwiP8TmGZuBioAl4TURmAeOA6cBEezt/EpGXjDGv5/F4FUWJAGEcS6YlSpxwmIbC8ke+\nhaUVSywcGoDdPuu8aYzpA7aLyGZgKvAJ4C1jzAkAEXkRWAAMEpZ777038XrRokUsWrQod2egKErB\nCZNjgcwGyzrCUlVV5TvN9enK8uXLWb58eUH2lW9hWQk0i8gkrJDWF4ElnnWesZc9LiJnYInKNuAs\n4O9F5J+wQnaXAD/x24lbWBRFKX6S9QpzhKW3tzejcRktLS08+eSTnHvuucPKsXgfupcuXZq3feU1\nx2KM6QW+DrwMrAeeMsZsFJGlIvJZe52XgHYRWQ+8CnzLGHMI+B2WwKwF3gPeM8a8kM/jVRQlGqRy\nLOlO9OVm7ty5rF27lqNHj2ryPk/kfWpiY8wfgbM9y77veX8HcIdnWR9wc76PT1GU6JEqx3L8+HFO\nnDhBdXV12tuura2lrq6ONWvWsHjx4lwdsuJCR94rihI5kvUKi8fjHD9+nHg8TmlpaUbbP/fcc1m7\ndu2wCoUVEhUWRVEiRzLHUlZWRllZWVblfVpaWujr61NhyRMqLIqiRA4nee/nWMAKh2VTULGlpQVA\nhSVPqLAoihI5kjkWsIQlW8cCKiz5QoVFUZTIkaxXGGQvLA0NDYwdOzaj5L+SGhUWRVEiR3l5OT09\nPZw4cSIvjkVEeOONNzjrrLOyOUwlgLx3N1YURUkXESEWi3HkyJG85FgApk2bltX3lWDUsSiKEkni\n8TgdHR15cSxKflFhURQlklRUVAQ6lng8rsISYVRYFEWJJBUVFepYihQVFkVRIokjLPnKsSj5Q4VF\nUZRI4oTC1LEUH9orTFGUSJLMsVx66aU0NzcPwVEpYVBhURQlksTjcXp7e30dyw033DAER6SERUNh\niqJEEsep+DkWJdqosCiKEkkcQfFzLEq0UWFRFCWSqGMpXlRYFEWJJOpYihcVFkVRIokjKOpYig8V\nFkVRIok6luJFhUVRlEhSUVFBSUkJZWU6KqLYUGFRFCWSVFRUqFspUlRYFEWJJBUVFZpfKVJUWBRF\niSTqWIoXFRZFUSJJPB5Xx1KkqLAoihJJ1LEULyosiqJEEs2xFC8qLIqiRBJ1LMVL3oVFRK4QkU0i\nskVE7gpY51oRWS8ia0XkCXvZIhF5T0T+Zv9/QkSuyvfxKooSDdSxFC95FRYRKQF+BlwOzAKWiMh0\nzzrNwF3AQmPMHOB2AGPMcmPMucaY84BPAseAl/N5vMXO8uXLh/oQIoNei36K9VrkI3lfrNei2Mi3\nY5kHbDXG7DDGdANPAVd71rkR+Lkx5giAMeaAz3Y+B7xojOnK69EWOfqj6UevRT/Fei1mzpzJZz7z\nmZxus1ivRbGR71oJ9cBO1/tWLLFxMw1ARF7HErqlxpiXPOt8Efjv+TpIRVGix/Tp05k+fXrqFZXI\nkW9hEZ9lxucYmoGLgSbgNRGZ5TgYEZkAzAa8YqMoiqJEEDHG287ncOMiC4B7jTFX2O/vBowx5j7X\nOr8A3jTGPG6//xNwlzHmXfv9N4GZxpibA/aRvxNQFEU5jTHG+D38Z02+HctKoFlEJgF7sEJaSzzr\nPGMve1xEzgCmAttcny8B7g7aQb4ujKIoipIZeU3eG2N6ga9j9eZaDzxljNkoIktF5LP2Oi8B7SKy\nHngV+JYx5hCALUgNxpgV+TxORVEUJXfkNRSmKIqiDD+KeuR9mMGXxY6INIjIMhHZYA8g/aa9fLSI\nvCwim0XkJRGpdX3nf4jIVhFZJSItruU32Ndqs4h8eSjOJ1tEpMQeNPuc/f5MEXnLPqcnRaTMXh4T\nkafs6/CmiDS5tvEde/lGEfn0UJ1LtohIrYg8bZ/HehGZPxzvCxH5BxFZJyJrROS39t9+2NwXIvKI\niOwTkTWuZTm7D0TkPPvabhGRn4Y6KGNMUf7DEsX3gUlAObAKmD7Ux5WH85wAtNivRwCbgenAfcC3\n7eV3Af9kv74SeMF+PR94y349GvgAqAVGOa+H+vwyuB7/ADwBPGe//9/A5+3XvwBusl/fAjxgv/4C\nVhgWYCbwHlZ+8Uz7HpKhPq8Mr8VjwFft12X233ZY3RfARKycbMx1P9wwnO4L4ONAC7DGtSxn9wHw\nNjDPfv3vwOUpj2moL0oWF3MB1qBJ5/3dWL3JhvzY8nzezwCXAZuA8fayCcBG+/WDwBdc628ExmN1\nnPiFa/kv3OsVwz+gAXgFWES/sOwHSrz3BPBHYL79uhRo87tPgBed9YrpHzAS+MBn+bC6L2xh2WE3\njGXAc8CngLbhdF9gPWC7hSUn94H93Q2u5QPWC/pXzKEwv8GX9UN0LAVBRM7EejJ5C+um2QdgjNkL\n1NmrBV0X7/JdFN/1+glwJ/ZYKBEZCxwyxvTZn7vvgcT5GqsTSYeIjOH0uA4AU4ADIvKvdmjwIRGp\nYpjdF8aY3ViDpz/COvYO4G/A4WF6XzjU5eg+qLfX8a6flGIWljCDL08bRGQE8DvgNmPMUYLP1Xtd\nxF63qK+XiCwG9hljVtF/LsLg8zKuz7wU/XVwUQach1UO6TysWnp3M/zui1FYZaImYbmXaqxwj5fh\ncl+kIt37IKPrUszC0oo1Ut+hAdg9RMeSV+zE4++A3xhjnrUX7xOR8fbnE7CsP1jXpdH1dee6FPv1\nugi4SkS2AU9iFSb9KVArVrFTGHhOiesgIqVY8eJDBF+fYqMV2GmM+av9/t+whGa43ReXAduMMQdt\nB/IH4EJg1DC9LxxydR9kdF2KWVgSgy9FJIYV+3tuiI8pXzyKFee837XsOeAr9uuvAM+6ln8ZEpUP\nDtuW+CXgU3ZPotFYceiiKZNjjPmuMabJGDMF62+9zBhzPfBn4PP2ajcw8DrcYL/+PLDMtfyLdu+g\nyVjlhN4pxDnkEvtvulNEptmLLsUaKzas7gusENgCEYmLiNB/HYbbfeF17zm5D+ww2hERmWdf3y+7\nthXMUCedskxYXYHVS2orcPdQH0+ezvEioBer19t7WPHjK4AxwJ/s838FGOX6zs+werWsBs5zLf+K\nfa22AF8e6nPL4ppcQn/yfjJWr5UtWD2Byu3lFcD/sc/3LeBM1/e/Y1+fjcCnh/p8srgOc7EesFYB\nv8fq0TPs7gvg+/bfcg3wa6xeosPmvgD+F5aLOIkltF/F6syQk/sA+A/AWvuz+8Mckw6QVBRFUXJK\nMYfCFEVRlAiiwqIoiqLkFBUWRVEUJaeosCiKoig5RYVFURRFySkqLIqiKEpOUWFRFBci0mn/P0lE\nvLOdZrvt73jev57L7StKVFBhUZSBOAO7JgNfSueLrhIiQXx3wI6M+Xg621eUYkGFRVH8+RHwcbty\n8G32BGP/LCJv2xMk3QggIpeIyF9E5Flgg73sDyKyUqyJ2f7eXvYjoNLe3m/sZZ3OzkTkX+z1V4vI\nta5t/1n6J/P6TYGvgaJkRNlQH4CiRJS7gTuMMVcB2EJy2Bgz365N9/9E5GV73XOBWcaYj+z3XzXG\nHBaROLBSRP7NGPMdEfmvxqpE7OCU///PwDnGmDkiUmd/Z4W9TgvWJFR77X1eaIx5I58nrijZoo5F\nUcLxaeDLIvIeVg2qMcBU+7N3XKICcLuIrMKqRdXgWi+Ii7AqNmOMaQOWAxe4tr3HWLWXVmHNbqgo\nkUYdi6KEQ4BvGGNeGbBQ5BKsuVDc7z+JNfvgSRH5MxB3bSNo20HvT7pe96K/WaUIUMeiKANxGvVO\nrOl/HV4CbrXnxkFEptozNnqpxZrV8qSITMeaFtfhlPN9z77+AnzBzuOMA/4jxVWyXVEGoE8/ijIQ\np1fYGqDXDn09Zoy5354a+m/2vBRtwH/y+f4fgZtFZD1WyfI3XZ89BKwRkXeNMf/F2Zcx5g/23Bir\ngT7gTmNMm4jMCDg2RYk0WjZfURRFySkaClMURVFyigqLoiiKklNUWBRFUZScosKiKIqi5BQVFkVR\nFCWnqLAoiqIoOUWFRVEURckpKiyKoihKTvn/Bsbq728pzT4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c281a2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pr61JnpUrV7J3797Q72effTajRo1yfL5hhQEsWbKE448/nl/96lfk5OREPae1tZW8vDxm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\nNBqNRuNR/j/VrJ7eIKODTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3c28119e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot loss over time\n",
    "plt.plot(i_data, train_loss, 'k-', label='Train Loss')\n",
    "plt.plot(i_data, test_loss, 'r--', label='Test Loss', linewidth=4)\n",
    "plt.title('Cross Entropy Loss per Iteration')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cross Entropy Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n",
    "\n",
    "# Plot train and test accuracy\n",
    "plt.plot(i_data, train_acc, 'k-', label='Train Set Accuracy')\n",
    "plt.plot(i_data, test_acc, 'r--', label='Test Set Accuracy', linewidth=4)\n",
    "plt.title('Train and Test Accuracy')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our predictions are not very accurate.  More time must be spent in tuning the CBOW embeddings.  We can also try to deepen our model structure as well.\n",
    "\n",
    "Also, sentiment analysis is really hard."
   ]
  }
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